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Found 7 papers, 0 papers with code
Random Linear Projections Loss for Hyperplane-Based Optimization in Neural Networks
Advancing loss function design is pivotal for optimizing neural network training and performance.
Counterfactual Data Augmentation with Contrastive Learning
To address this, we introduce a model-agnostic data augmentation method that imputes the counterfactual outcomes for a selected subset of individuals.
Individual Treatment Effects in Extreme Regimes
Understanding individual treatment effects in extreme regimes is important for characterizing risks associated with different interventions.
Causal Mediation Analysis with Multi-dimensional and Indirectly Observed Mediators
This is important in many scientific applications to identify the underlying mechanisms of a treatment effect.
Mode-Aware Continual Learning for Conditional Generative Adversarial Networks
To this end, we introduce a new continual learning approach for conditional generative adversarial networks by leveraging a mode-affinity score specifically designed for generative modeling.
Domain Adaptation via Rebalanced Sub-domain Alignment
To address this limitation, we propose a novel generalization bound that reweights source classification error by aligning source and target sub-domains.
Transfer Learning for Individual Treatment Effect Estimation
To this end, we theoretically assess the feasibility of transferring ITE knowledge and present a practical framework for efficient transfer.
